Blur is one of the most important features related to image quality. Accurately estimating the blur level of an image is a great help to accurately evaluate its quality. Various methods have been proposed to solve the problem, for example in “No-Reference Block Based Blur Detection”1 by Debing Liu, Zhibo Chen, Huadong Ma, Feng Xu and Xiaodong Gu, or in “A no-reference perceptual blur metric”2 by P. Marziliano, F. Dufaux, S. Winkler, and T. Ebrahimi, or in a similar patent application WO03/092306. Proc. 2009 International Workshop on Quality of Multimedia Experience, pp. 75-80Proc. of IEEE Int. Conf. on Image Processing, vol. 3, September 2002, pp. 57-60
All currently known blur detection schemes estimate the blur level of an image just from the information of the image itself. However, it is difficult to design a general blur detection scheme that has high performance for all kinds of blur. Blur detection is used for image quality determination or estimation, or before blur cancelling. For both, it is desirable to know what the source of the blur is. From images alone this is difficult or even impossible. Therefore general blur detection schemes are currently used. Because such general solution cannot take account of the specific blur types in different cases, its accuracy is not good.